2016 Speech Information Processing

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Academic unit or major
Graduate major in Artificial Intelligence
Shinoda Koichi 
Class Format
Media-enhanced courses
Day/Period(Room No.)
Mon7-8(W831,G111)  Thr7-8(W831,G111)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

This course focuses on speech information processing. It first teaches basic knowledge about human speech and natural languages, then, introduces speech information processing by machine. Next, it explains the elements of automatic speech recognition systems which include acoustic models, language models, search algorithms, and some techniques to improve the performance and robustness of the systems such as optimization, adaptation, discriminative training. Finally, it introduces other applications of speech information processing such as speech synthesis, speaker recognition.

Student learning outcomes

At the end of this course, students will be able to:
1) explain the mechanism of human speech production and perception,
2) explain each component of speech recognition systems,
3) have an understanding of the importance of probabilistic modeling in speech recognition and explain its training and recognition algorithm ,
4) build a speech recognition system by their own.


speech information processing, speech recognition, speech analysis, speech coding, speech synthesis, acoustic models, language models, search algorithms, graphical models, hidden Markov models

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

1) At the beginning of each class, the contents of the previous class are reviewed.
2) At the end of each class, an assignment is given, which should be submitted in the next class.
3) Attendance is taken in every class.
4) Students are recommended to learn the topics by themselves before coming to class.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Speech and Language Explain in the class.
Class 2 Speech analysis, Speech coding Explain in the class.
Class 3 Introduction of speech recognition Explain in the class.
Class 4 Graphical models Explain in the class.
Class 5 Hidden Markov models Explain in the class.
Class 6 Recognition and training algorithms Explain in the class.
Class 7 Language models Explain in the class.
Class 8 Search algorithms Explain in the class.
Class 9 Optimization, adaptation Explain in the class.
Class 10 Noise robustness Explain in the class.
Class 11 Discriminative training for speech recognition Explain in the class.
Class 12 Speech recognition applications Explain in the class.
Class 13 Speech synthesis, voice conversion Explain in the class.
Class 14 Speaker recognition Explain in the class.
Class 15 Future prospects Explain in the class.



Reference books, course materials, etc.

S.Furui 著 『Digital Speech Processing,Synthesis,and Recognition』 Mercel Dekker

Assessment criteria and methods

Students course scores are based on an assignment in every class (20% in total) and two reporting assignments (80% in total).

Related courses

  • ART.T547 : Multimedia Informaiton Processing

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Students are required to have the knowledge on computer science of undergraduate levels.



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